4.7 Article

Physical characteristics and environmental risks assessment of oil-based drilling cuttings residues used for subgrade materials

期刊

JOURNAL OF CLEANER PRODUCTION
卷 323, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2021.129152

关键词

ODCRs; Hazardous waste; Subgrade materials; Mechanical characteristics; Risk assessment

资金

  1. Chongqing Natural Science Foundation, China [cstc2018jcyjAX0043]

向作者/读者索取更多资源

Oil-based drilling cuttings residues (ODCRs) can be used as subgrade materials for well site construction, reducing costs and solving practical issues. However, ODCRs contain carcinogenic pollutants and have some environmental risks, even though they meet the requirements for building materials. The addition of cement increases the unconfined compressive strength of ODCR subgrade materials.
Oil-based drilling cuttings residues (ODCRs) can be used as subgrade materials for well site construction. It not only reduces the cost of shale gas development, but also solves the practical problems encountered in shale gas development. The results show that ODCRs contained many carcinogenic pollutants, such as As, Benzo anthracene (a), Benzo pyrene (a), Dibenzo anthracene (a, h). ODCRs have some environmental risks. However, the main mineral components and radioactive strength of ODCRs meets the requirements of Class A materials, which can be used for building materials. The experimental results show that the unconfined compressive strength of ODCRs subgrade materials increases with the increase of cement content. The unconfined compressive strength and compactness first increase and then decrease with the increase of ODCRs content. The optimal content of cement is 4%, and the optimal content of ODCRs is 60%. In addition, ODCRs have pozzolanic characteristics. Active SiO2 and Al2O3 in ODCRs can react with Ca(OH)(2) to produce a certain amount of gel materials, such as ettringite (AFt) and gel hydrated calcium silicate (C-S-H). These gel materials can form a network structure to encapsulate heavy metals and macromolecular organic matters. So, no organic pollutants and some heavy metals are detected in the leaching solution of subgrade materials with 60% ODCRs, and there will be no secondary pollution to the external environment.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
Article Green & Sustainable Science & Technology

Relative evaluation of probabilistic methods for spatio-temporal wind forecasting

Lars odegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, Paal Engelstad

Summary: This study investigates uncertainty modeling in wind power forecasting using different parametric and non-parametric methods. Johnson's SU distribution is found to outperform Gaussian distributions in predicting wind power. This research contributes to the literature by introducing Johnson's SU distribution as a candidate for probabilistic wind forecasting.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

Comparison of ethane recovery processes for lean gas based on a coupled model

Xing Liu, Qiuchen Wang, Yunhao Wen, Long Li, Xinfang Zhang, Yi Wang

Summary: This study analyzes the characteristics of process parameters in three lean gas ethane recovery processes and establishes a prediction and multiobjective optimization model for ethane recovery and system energy consumption. A new method for comparing ethane recovery processes for lean gas is proposed, and the addition of extra coolers improves the ethane recovery. The support vector regression model based on grey wolf optimization demonstrates the highest prediction accuracy, and the multiobjective multiverse optimization algorithm shows the best optimization performance and diversity in the solutions.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

A novel deep-learning framework for short-term prediction of cooling load in public buildings

Cairong Song, Haidong Yang, Xian-Bing Meng, Pan Yang, Jianyang Cai, Hao Bao, Kangkang Xu

Summary: The paper proposes a novel deep learning-based prediction framework, aTCN-LSTM, for accurate cooling load predictions. The framework utilizes a gate-controlled multi-head temporal convolutional network and a sparse probabilistic self-attention mechanism with a bidirectional long short-term memory network to capture both temporal and long-term dependencies in the cooling load sequences. Experimental results demonstrate the effectiveness and superiority of the proposed method, which can serve as an effective guide for HVAC chiller scheduling and demand management initiatives.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

The impact of social interaction and information acquisition on the adoption of soil and water conservation technology by farmers: Evidence from the Loess Plateau, China

Zhe Chen, Xiaojing Li, Xianli Xia, Jizhou Zhang

Summary: This study uses survey data from the Loess Plateau in China to evaluate the impact of social interaction on the adoption of soil and water conservation (SWC) technology by farmers. The study finds that social interaction increases the likelihood of farmers adopting SWC, and internet use moderates this effect. The positive impact of social interaction on SWC adoption is more pronounced for farmers in larger villages and those who join cooperative societies.

JOURNAL OF CLEANER PRODUCTION (2024)

Article Green & Sustainable Science & Technology

Study on synergistic heat transfer enhancement and adaptive control behavior of baffle under sudden change of inlet velocity in a micro combustor

Chenghua Zhang, Yunfei Yan, Kaiming Shen, Zongguo Xue, Jingxiang You, Yonghong Wu, Ziqiang He

Summary: This paper reports a novel method that significantly improves combustion performance, including heat transfer enhancement under steady-state conditions and adaptive stable flame regulation under velocity sudden increase.

JOURNAL OF CLEANER PRODUCTION (2024)